The OpportunityAs our ML Infrastructure Tech Lead, you'll own the systems that make high-performance model training and inference possible at Reducto.
This is a deeply hands-on role: roughly 80% of your time will be spent building, debugging, and optimizing our infrastructure. The remaining 20% will focus on setting technical direction - identifying bottlenecks, planning our infrastructure roadmap, and helping the ML and Platform teams make strong architectural decisions.
You'll work across the stack, from model-serving kernels and GPU utilization to distributed systems and Kubernetes. We're looking for someone with the experience and judgment to lead ambiguous, high-impact infrastructure projects while remaining close to the code.
This is a fully in-person role at our San Francisco office.
What You'll Do- Own the technical direction and roadmap for Reducto's ML infrastructure.
- Build and maintain our training and inference stack, balancing fast experimentation with high-performance production serving.
- Optimize model serving at every layer, including kernels, runtimes, batching, scheduling, and distributed inference.
- Design systems for reliable multi-node, multi-GPU training and inference.
- Improve GPU utilization, latency, throughput, reliability, observability, and cost efficiency.
- Develop benchmarks that identify bottlenecks and guide infrastructure investments.
- Evaluate state-of-the-art advances in training and inference and apply the ones that matter.
- Build the tooling and abstractions that help ML engineers move quickly from experiments to production.
- Partner with ML and Platform engineers on architecture, capacity planning, and technical prioritization.
- Raise the engineering bar through design reviews, mentorship, and hands-on technical leadership.
You'll Thrive Here If You- Have 5+ years of experience building production infrastructure, including significant ML systems experience.
- Have led complex technical projects from an ambiguous problem through production deployment.
- Are equally comfortable setting direction and personally implementing the hardest parts.
- Have strong Python and systems-engineering skills.
- Understand the performance characteristics of modern GPU training or inference workloads.
- Are comfortable with Kubernetes and distributed training or serving frameworks.
- Can reason across low-level model performance and higher-level platform architecture.
- Hold yourself to a high bar for quality, precision, and operational reliability.
- Operate well in a fast-changing, high-growth environment.
- Take full ownership from strategy through execution.
Bonus Points If You- Have optimized or implemented CUDA, Triton, or custom model-serving kernels.
- Have contributed meaningfully to frameworks such as vLLM, SGLang, PyTorch, TensorRT-LLM, Ray, or related open-source systems.
- Have operated distributed inference or training across hundreds or thousands of GPUs.
- Have built observability, scheduling, or capacity-management systems for GPU workloads.
- Have experience at an early-stage or high-growth startup.
- Care deeply about connecting technical excellence to measurable business impact.
Why Reducto- Impact: Your work directly shapes how the world's best AI companies access and use enterprise data.
- Speed: We move fast, ship often, and iterate in days, not months.
- Learning: Work alongside world-class engineers, operators, and founders who care deeply about product, precision, and velocity.
Benefits- Unlimited PTO, because great work requires recharging.
- Daily Lunch, enjoy free lunch with teammates in the office.
- Commuter Reimbursement, we'll cover your transportation costs.
- Comprehensive Insurance, medical, dental, and vision.
- Health and Wellness Budget, up to $150 per month for wellness spending such as gym memberships or fitness classes.
- Parental Leave, flexible scheduling that works for you and your family.Working at Reducto
This is an in-person role at our San Francisco office. We're an early-stage company, which means the role requires working hard and moving quickly. Please only apply if that excites you.
